Aperiodic Servers (Issues)

Transcription

1 Aperiodic Servers (Issues) Interference Causes more interference than simple periodic tasks Increased context switching Cache interference Accounting Context switch time Again, possibly more context switches than a simple periodic task 1

2 Aperiodic Servers (Issues) Inefficiencies Competition for use of budget Lower response time or increase throughput Cache effects Fragmentation of server time may mean increased cache misses, resulting in lower efficiency of server's CPU time 2

3 Aperiodic Servers (Issues) Guarantees for aperiodic jobs (NOT HANDLED) e.g Minimum response time based on a given input model Assume aperiodic job timing guarantees are a separate issue For now assume: No timing constraints for aperiodic jobs Desire reasonable average response time 3

4 Context Switching Accounting CS time (direct) Interference CS cache effects (indirect) On other tasks on the system On the server's time budget 4

5 Context Switching without interference (isolated) Increase in execution time due to cache misses with interference (other tasks) time context switch time 5

6 Context Switch Time (Direct) Context switch time Preemptions and resumptions cause a context switch Simple periodic task can preempt once per period for fixed priority Equates to two context switches per task per period Bandwidth-preserving aperiodic servers can preempt much more (cause many more CSs) 6

7 CS Time Accounting (Direct) CS time must be accounted for in schedulability analysis Included in the interference calculation These context switches can be logically charged to the preempting task by considering the CS time as part of the WCET of the preempting task Accounting for CS time in the aperiodic server and can be done Offline Online 7

8 Including CS time (Direct) Offline Number of CSs Maximum number of replenishments * 2 WCET = max execution time + CS time Server budget = max execution time Pessimistic May not use all CS time e.g. server budget may be used all at once Reserved CS time could have been used to perform work 8

9 Including CS time (Direct) Online WCET = Max Execution time + CS time Server budget = WCET More time available to server More accurate accounting Use actual (measured) CS time rather than worst-case CS time Better use of time included in the schedulability analysis Unlike offline unused CS time, time can be used to perform actual work 9

10 Including CS time (Direct) WCET for both online and offline are inflated However, server is able to reclaim pre-allocated CS time in the online approach Unlike offline approach, unused CS time, time can be used to perform actual work 10

11 Including CS time time time If only one CS is performed the 'unused' CS time can be used to perform work time 11

12 Cache Interference Preemption may cause the execution time of a given job to increase due to cache interference Cache Related Preemption Delay (CRPD) The amount of execution time a given job will be increased when preempted by another job on the system 12

13 Cache Interference Cache misses due to context switching cause interference for: Other tasks besides the aperiodic server The aperiodic server itself Increased fragmentation of the aperiodic server's time increases the cache misses 13

14 Sporadic Server Theoretical sporadic server Infinite replenishments No overhead for switching tasks Implemented sporadic server Maximum value on the number of replenishments Overhead becomes larger as tasks are switched more frequently Increased time spent context switching Increased number of cache misses 14

15 Cache Interference/CS Overhead job arrival job completion time time replenishment 15

16 Polling Server = aperiodic job arrival = aperiodic job CPU time time 16

17 Sporadic Server = aperiodic job arrival = replenishment period = aperiodic job CPU time time 17

18 Sporadic Server From this point on, if the server is using all its budget, the execution pattern will be locked in time 18

19 Budget =.7 msec Period = 10 msec Max Repl. = 150 Response Time 19

20 Budget =.7 msec Period = 10 msec Max Repl. = 150 Dropped Packets 20

21 Analysis Light load Sporadic Server Low response time Polling Server High response time Heavy load Sporadic Server High response time Dropped packets Polling Server Low response time No dropped packets 21

22 Analysis Light load Sporadic Server Low response time Polling Server High response time Heavy load Sporadic Server High response time Dropped packets Polling Server Low response time No dropped packets 22

23 Can we get the best of both? Sporadic Server Light loads Polling Server Heavy loads 23

24 Forced Coalescing Under heavy loads, fragmentation causes inefficient use of the CPU Fragmentation is continued as long as SS is using all its budget (locked in execution pattern) Solution Force some number of fragments to be merged while the server is overloaded (i.e. using all its budget) 24

25 Sporadic Server time 25

26 Sporadic Server time 26

27 Sporadic Server time 27

28 Budget =.7 msec Period = 10 msec Max Repl. = 150 Forced Coalescing 28

29 Budget =.7 msec Period = 10 msec Max Repl. = 150 Forced Coalescing 29

30 Budget =.9 msec Period = 10 msec Max Repl. = 150 Forced Coalescing Large number of context switches. Coalescing occurs and number of fragments is reduced. 30

31 Forced Coalescing Self tuning Merging of replenishments will occur until server does not use all of its budget (not overloaded) Allows some fragmentation which will help with reducing response time Will continue to merge if overloaded until the server is effectively a polling server 31

32 Minimum Separation time 32

33 Minimum Separation Between each usage of CPU time, force a minimum time separation Bounds context switches experienced by other lower priority tasks Compare overhead with Cache Delay Server Better coalescing of aperiodic jobs Improved CPU efficiency Less time spent context switching More likely to execute out of cache 33